Zhun Li, HyeYoung Lee, Youngjun Lee, Sooji Yoon, Byeongil Bae, Ho-Jin Choi, Journal of Internet Computing and Services, Vol. 20, No. 1, pp. 25-37, Feb. 2019
10.7472/jksii.2019.20.1.25, Full Text:  HTML
Keywords: Deep Learning, Handwriting Recognition, Writer Verification, Authentication System, One-time password, FIDO

Abstract

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user’s handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

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